Optimization gaussian noise removal using hybrid filter based on mean impulse fuzzy and fuzzy aliasing filter methods

This research aimed to reduce noise in the image corrupted by Gaussian noise. Gaussian noise is able change the image pixel data as a whole. We introduced the Hybrid Filter based on fuzzy methods to reduce the Gaussian noise. The hybrid filter combines Fuzzy Aliasing Filter (FAF) and Mean Impulse Fuzzy (MIF). MIF is a filter that processes the degree of linear membership function on the degraded images, which use 3×3 window. Meanwhile, the highest and lowest value of an element in the 3×3 window was replaced with the average value of an element in the 3×3 windows without includes the highest and lowest elements in the calculation. MIF method was more suitable for the variance noise content more than 20%. Conversely, the content of the noise variance less than 20% used FAF. The degree of membership function value on FAF was obtained from the Gaussian membership function. FAF method adopted Aliasing Filter technique and the linear approach, which used the mean value of the regional block. The quality of Hybrid Filter was compared to the Weighting Mean Filter, Adaptive Wiener Filer, Optimum Aliasing Filter and Optimum Weighting Gaussian Aliasing Filter. Our method was optimal to reduce Gaussian noise.